Standard VisDa-17: We also perform experiments for UDA where there is no labeled data in the target domain. In Table REF , we compare the results of our method with state-of-the-art unsupervised domain adaptation methods. We find that our method is very competitive with other methods despite being conceptually simpler. Moreover, most of the methods we compare against involve adversarial training which can be difficult to train. Apart from difficulty in training, some of the methods e.g., DTA [1]}, can involve up to 5 different loss components which means additional 5 hyper-parameters to tune. Even our most basic method, KD(ROT), is comparable to more involved methods like MCD [2]} and CDAN [3]}. We also observe significant boost in performance when our method is combined with entropy minimization (ENT) [4]} and virtual adversarial training (VAT) [5]}. We show that it is possible to achieve second best performance on standard VisDa-17 benchmark with VAT and entropy minimization added to our method. VAT is a regularization method introduced in [5]} that encourages the model to be smooth around each unlabeled data-point.
<TABLE>